A variety of discrete pixel imaging techniques are known and are presently in use. In general, such techniques rely on the collection or acquisition of data representative of each discrete pixel composing an image matrix. In the medical imaging field, for example, several modalities are available for producing the data represented by the pixels, including magnetic resonance techniques, X-ray techniques, and so forth. Depending upon the particular modality employed, the pixel data is detected and encoded, such as in the form of digital values. The values are linked to particular relative locations of the pixels in the reconstructed image.
The utility of a processed image is often largely dependent upon the degree to which it can be interpreted by users. For example, in the field of medical diagnostics imaging, MRI, X-ray, and other images are most useful when they can be easily understood and compared by an attending physician or radiologist. Moreover, while a number of image processing parameters may control the final image presentation, it is often difficult to determine which of these parameters, or in what combination the parameters may be adjusted to provide the optimal image presentation. Often, the image processing techniques must be adjusted in accordance with empirical feedback from the physician.
The facility with which a reconstructed discrete pixel image may be interpreted by an observer may rely upon intuitive factors of which the observer may not be consciously aware. For example, a physician or radiologist may seek specific structures or specific features in an image. In medical imaging, such features might include bone, soft tissue or fluids. Such structures may be physically defined in the image by contiguous edges, contrast, texture, and so forth. The presentation of such features often depends heavily upon the particular image processing technique employed for converting the detected values representative of each pixel to modified values used in the final image. The signal processing technique employed can therefore greatly affect the ability of the observer to visualize salient features of interest. Ideally, the technique should carefully maintain recognizable structures of interest, as well as abnormal or unusual structures, while providing adequate textural and contrast information for interpretation of these structures and surrounding background.
Known signal processing systems for enhancing discrete pixel images suffer from certain drawbacks. For example, such systems may not consistently provide comparable image presentations in which salient features or structures may be easily visualized. Differences in the reconstructed images may result from particularities of individual scanners and circuitry, as well as from variations in the detected parameters (e.g. molecular excitation or received radiation). Differences can also result from the size, composition and position of a subject being scanned. Signal processing techniques employed in known systems are often difficult to reconfigure or adjust, owing to the relative inflexibility of hardware or firmware devices in which they are implemented or to the coding approach employed in software. Finally, known signal processing techniques often employ computational algorithms which are not particularly efficient, resulting in delays in formulation of the reconstituted image or under-utilization of signal processing capabilities.
Moreover, certain known techniques for image enhancement may offer excellent results for certain systems, but may not be as suitable for others. For example, low, medium and high field MRI systems may require substantially different data processing due to the different nature of the data defining the resulting images. In current techniques completely different image enhancement frameworks are employed in such cases. In addition, current techniques may result in highlighting of small, isolated area which are not important for interpretation and may be distracting to the viewer. Conversely, in techniques enhancing images by feature structure recognition, breaks or discontinuities may be created between separate structural portions, such as along edges. Such techniques may provide some degree of smoothing or edge enhancement, but may not provide satisfactory retention of textures at ends of edges or lines.
There is a need, therefore, for an improved technique for enhancing discrete pixel images which addresses these concerns.